Today, drug response for some patients may be predicted based on a patient's coding genome. Specific genetic traits may be mapped to a particular response to a drug and a drug may be selected for a patient based on the patient's predicted response.
However, noncoding genomic variants account for the vast majority of genetic differences for traits such as drug response, adverse drug response, and disease risk in patients. The convergence of epigenomic regulation research and genome wide association studies (GWAS) has also shown that epigenomic alterations may be indicators of disease risk, drug response, and adverse drug response in both human and animals, in a broad set of medical specialties and pharmacological research settings. Moreover, disease-related phenotype variation may be dictated by differences in chromatin state which had previously been attributed to genetic differences.
Current systems do not utilize chromatin state, genomic regulatory elements, epigenomics, proteomics, metabolomics, or transcriptomics to predict pharmacological phenotypes for patients. Current systems also do not factor in environmental and sociological characteristics that may alter genetic traits for determining the pharmacological phenotypes. Additionally, such systems do not utilize machine learning techniques to train the systems to adapt to changes in biological characteristics and/or pharmacological phenotypes corresponding to the biological characteristics over time.
Accordingly, there is a need for a system that accurately predicts pharmacological phenotypes including pharmacological response, disease risk, substance abuse or other pharmacological phenotypes based on panomic characteristics including genomics, epigenomics, chromatin state, proteomics, metabolomics, transcriptomics, etc., and sociological and environmental characteristics of a patient in near real-time.